1 Inspired by

https://www.guylipman.com/octopus/api_guide.html

and using

https://developer.octopus.energy/docs/api/#

2 Test without authentication

# test
url <- "https://api.octopus.energy/v1/products"
message("Getting: ", url)

## Getting: https://api.octopus.energy/v1/products

resp <- httr::GET(url)

message("Status code: ", resp$status_code)

## Status code: 200

df <- jsonlite::parse_json(resp, simplifyVector = TRUE)

## No encoding supplied: defaulting to UTF-8.

makeFlexTable(head(df$results), cap = "Example products list (first 6 rows)")

Table 2.1: Example products list (first 6 rows)
code	direction	full_name	display_name	description	is_variable	is_green	is_tracker	is_prepay	is_business	is_restricted	term	available_from	links	brand
AFFECT-FIX-12M-22-04-21	IMPORT	Affect 12M Fixed April 2022 v1	Affect 12M Fixed	This tariff features 100% renewable electricity and fixes your unit rates and standing charge for 12 months. There are no exit fees, so if you change your mind, you're in control.	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	12	2022-04-21T00:00:00+01:00	[[data.frame]]	AFFECT_ENERGY
AFFECT-OCC-VAR-21-10-01	IMPORT	Affect Occupier Standard Tariff October 2021 v1	Affect Occupier Standard Tariff	Affect Occupier Standard Tariff offers great value and 100% renewable electricity. As a variable tariff, your prices can rise and fall with wholesale prices - but we'll always give you notice of a change, and you can switch to a fixed tariff at any time.	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE		2021-10-01T00:00:00+01:00	[[data.frame]]	AFFECT_ENERGY
AFFECT-SEG-FIX-12M-20-11-11	EXPORT	Affect Smart Export Guarantee November 2020 v1	Affect Smart Export Guarantee	This is our Smart Export Guarantee compliant export tariff	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	12	2020-11-11T17:00:00Z	[[data.frame]]	AFFECT_ENERGY
AFFECT-VAR-22-04-02	IMPORT	Affect Standard Tariff April 2022 v1	Affect Standard Tariff	Affect Standard Tariff offers great value and 100% renewable electricity. As a variable tariff, your prices can rise and fall with wholesale prices - but we'll always give you notice of a change, and you can switch to a fixed tariff at any time.	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE		2022-03-05T00:00:00Z	[[data.frame]]	AFFECT_ENERGY
AGILE-18-02-21	IMPORT	Agile Octopus February 2018	Agile Octopus		TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	12	2017-01-01T00:00:00Z	[[data.frame]]	OCTOPUS_ENERGY
AGILE-OUTGOING-19-05-13	EXPORT	Agile Outgoing Octopus May 2019	Agile Outgoing Octopus	Outgoing Octopus Agile rate pays you for all your exported energy based on the day-ahead wholesale rate.	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	12	2018-01-01T00:00:00Z	[[data.frame]]	OCTOPUS_ENERGY

3 Tests with authentication

3.1 Basic info

url <- paste0("https://api.octopus.energy/v1/accounts/", apiParams$accountNo , "/")

resp <- httr::GET(url = url, authenticate(user = apiParams$key, password = ""))

df <- jsonlite::parse_json(resp, simplifyVector = TRUE)

## No encoding supplied: defaulting to UTF-8.

props <- data.table::as.data.table(df$properties)
makeFlexTable(head(props[, .(town, county, 
                             electricity_meter_points,gas_meter_points)]), cap = "Properties linked to this account (non-disclosive data)")

Table 3.1: Properties linked to this account (non-disclosive data)
town	county	electricity_meter_points	gas_meter_points
BRADFORD-ON-AVON	WILTSHIRE	[[data.frame]]	[[data.frame]]
FRAMLINGHAM		[[data.frame]]	[[data.frame]]

List the electricity meter points.

# this is a list of n mpans
length(props$electricity_meter_points)

## [1] 2

message("n MPANS listed: ", length(df$properties$electricity_meter_points))

## n MPANS listed: 2

for(n in 1:length(df$properties$electricity_meter_points)){
  print(props$electricity_meter_points[n])
}

## [[1]]
##            mpan profile_class consumption_standard
## 1 2000006198482             1                 7678
##                          meters
## 1 L78C64517, 01, STANDARD, TRUE
##                                                                                                                                                      agreements
## 1 E-1R-SUPER-GREEN-12M-20-09-22-H, E-1R-SUPER-GREEN-12M-20-09-22-H, 2020-11-01T00:00:00Z, 2020-11-21T00:00:00Z, 2020-11-01T00:00:00Z, 2021-06-30T00:00:00+01:00
##   is_export
## 1     FALSE
## 
## [[1]]
##            mpan profile_class consumption_standard
## 1 1050001805886             1                 3322
##                          meters
## 1 19L3027004, 1, STANDARD, TRUE
##                                                                                                                                                    agreements
## 1 E-1R-SUPER-GREEN-12M-20-09-22-A, E-1R-LOYAL-FIX-12M-21-10-07-A, 2021-07-01T00:00:00+01:00, 2021-11-21T00:00:00Z, 2021-11-21T00:00:00Z, 2022-11-21T00:00:00Z
##   is_export
## 1     FALSE

length(df$properties$gas_meter_points)

## [1] 2

message("n MPRNS listed: ", length(df$properties$gas_meter_points))

## n MPRNS listed: 2

for(n in 1:length(df$properties$gas_meter_points)){
  print(df$properties$gas_meter_points[n])
}

## [[1]]
##         mprn consumption_standard         meters
## 1 4256845702                19018 G4A01559730801
##                                                                                                                                                      agreements
## 1 G-1R-SUPER-GREEN-12M-20-09-22-H, G-1R-SUPER-GREEN-12M-20-09-22-H, 2020-11-01T00:00:00Z, 2020-11-21T00:00:00Z, 2020-11-01T00:00:00Z, 2021-06-30T00:00:00+01:00
## 
## [[1]]
##         mprn consumption_standard                                       meters
## 1 7825700304                15129 E6S12725512161, E6S17944211961, NOTINSTALLED
##                                                                            agreements
## 1 G-1R-LOYAL-FIX-12M-21-10-07-A, 2021-10-04T00:00:00+01:00, 2022-10-04T00:00:00+01:00

3.2 Electricity

3.2.1 Consumption

See: https://www.guylipman.com/octopus/api_guide.html#s3

url <- paste0("https://api.octopus.energy/v1/electricity-meter-points/", 
              apiParams$elec_import_mpan , "/",
              "meters/",
              apiParams$elec_import_serial, "/",
              "consumption/",
              "?period_from=2022-01-01T00:00Z",
              "&page_size=10000")
# get data via httr ----
resp <- httr::GET(url = url, authenticate(user = apiParams$key, password = ""))
df <- jsonlite::parse_json(resp, simplifyVector = TRUE) # creates a df of which 'results' = the data

## No encoding supplied: defaulting to UTF-8.

elecCons_dt <- data.table::as.data.table(df$results) # convert to dt

# derived variables ----
elecCons_dt <- makeDerivedVars(elecCons_dt)

maxTime <- max(elecCons_dt$dv_start)

hoursAgo <- now() - maxTime

# meter is SMETS2
elecCons_dt[, consumption_kWh := consumption] # for clarity - see https://developer.octopus.energy/docs/api/#list-consumption-for-a-meter

The data used here is up to 2022-06-15 23:30:00, which is 17.3 hours ago. In general the Octopus API seems to have data up to midnight last night.

t <- elecCons_dt[, .(nDays = uniqueN(dv_date),
                     sumkWh = sum(consumption_kWh),
                     halfHourly_meankWh = mean(consumption_kWh)),
                 keyby = .(month = lubridate::month(dv_date))]

makeFlexTable(t, cap = "Monthly stats")

Table 3.2: Monthly stats
month	nDays	sumkWh	halfHourly_meankWh
1.0	31	427.3	0.3
2.0	28	352.6	0.3
3.0	31	321.5	0.2
4.0	30	275.9	0.2
5.0	31	250.1	0.2
6.0	15	131.9	0.2

message("Total elec to date")

## Total elec to date

sum(t$sumkWh)

## [1] 1759.268

t <- elecCons_dt[, .(nDays = uniqueN(dv_date),
                     sumkWh = sum(consumption_kWh)),
                 keyby = .(dv_date)]
message("Projected elec annual total")

## Projected elec annual total

mean(t$sumkWh)*365

## [1] 3868.27

Figure 3.1 shows half-hourly electricity import (‘consumption’) for the current year. Spot the power cuts…

To do: mark weekends somehow

ggplot2::ggplot(elecCons_dt, aes(x = dv_date, y = dv_hms, fill = consumption_kWh)) +
  geom_tile() +
  theme(legend.position = "bottom") +
  scale_fill_viridis_c(name = "Electricity import (kWh)") +
  labs(x = "Date",
       y = "Half-hour")

Figure 3.1: Half hourly electricity import (current year)

Repeat but with just the last 7 days of data - useful for checking recent appliance use and offspring effects.

today <- lubridate::today()
p <- ggplot2::ggplot(elecCons_dt[dv_date >= today - 7], aes(x = dv_date, y = dv_hms, fill = consumption_kWh)) +
  geom_tile() +
  theme(legend.position = "bottom") +
  scale_fill_viridis_c(name = "Electricity import (kWh)") +
  labs(x = "Date",
       y = "Half-hour")

plotly::ggplotly(p)

Figure 3.2: Half hourly electricity import (current year, last 7 days)

plotDT <- elecCons_dt[, .(sum_kWh = sum(consumption_kWh),
                 mean_kWh = mean(consumption_kWh),
                 nObs = .N), keyby = .(dv_date, dv_weekend)]

ggplot2::ggplot(plotDT, aes(x = dv_date, y = mean_kWh, 
                            colour = dv_weekend)) +
  geom_point() +
  geom_smooth() +
  #facet_grid(dv_peakPeriod ~ .) +
  scale_colour_viridis_d(name = "Weekend") +
  labs(x = "Date",
       y = "Mean kWh")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 3.3: Daily electricity import (current year)

Figure 3.4 shows the mean daily kWh import with a smoothed curve for each period as defined below.

Early morning is effectively our baseload.

# check periods
t <- elecCons_dt[, .(min = min(dv_hms),
                max = max(dv_hms)),
            keyby = .(dv_peakPeriod)]

t

##    dv_peakPeriod      min      max
## 1: Early morning 00:00:00 06:30:00
## 2:  Morning peak 07:00:00 08:30:00
## 3:      Day time 09:00:00 15:30:00
## 4:  Evening peak 16:00:00 19:30:00
## 5:  Late evening 20:00:00 23:30:00

plotDT <- elecCons_dt[, .(sum_kWh = sum(consumption_kWh),
                 mean_kWh = mean(consumption_kWh),
                 nObs = .N), keyby = .(dv_date, dv_peakPeriod, dv_weekend)]

ggplot2::ggplot(plotDT, aes(x = dv_date, y = mean_kWh, 
                            colour = dv_peakPeriod)) +
  geom_line() +
  geom_smooth() +
  #facet_grid(dv_peakPeriod ~ .) +
  theme(legend.position = "bottom") +
  guides(colour = guide_legend (ncol = 3)) +
  scale_colour_viridis_d(name = "Peak period") +
  labs(x = "Date",
       y = "Mean kWh per period")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 3.4: Daily electricity import by peak period (current year)

3.2.2 Export

This will be a new MPAN but specified as export - although the url will still say consumption. We do not have this even though the PV is exporting on (some) days.

It may be that we only get this data if we sign up for the export tariff.

See https://www.guylipman.com/octopus/api_guide.html#s3

3.3 Gas Consumption

We need to convert the gas consumption from m3 to kWh - see https://developer.octopus.energy/docs/api/#list-consumption-for-a-meter

gasM3TokWh <- 11.36

We use 11.36 (https://www.theenergyshop.com/guides/how-to-convert-gas-units-to-kwh)

url <- paste0("https://api.octopus.energy/v1/gas-meter-points/", 
              apiParams$gas_mpan , "/",
              "meters/",
              apiParams$gas_serial, "/",
              "consumption",
              "?period_from=2022-01-01T00:00Z",
              "&page_size=10000")
resp <- httr::GET(url = url, authenticate(user = apiParams$key, password = ""))
df <- jsonlite::parse_json(resp, simplifyVector = TRUE)

## No encoding supplied: defaulting to UTF-8.

gasCons_dt <- data.table::as.data.table(df$results)
gasCons_dt <- makeDerivedVars(gasCons_dt)

# gas 'consumption' is m3 - https://developer.octopus.energy/docs/api/#list-consumption-for-a-meter
# convert to kWh
gasCons_dt[, consumption_m3 := consumption]
gasCons_dt[, consumption_kWh := consumption * gasM3TokWh]

Note that this data starts later as we finally got the original un-registered smart meter replaced in February.

t <- gasCons_dt[, .(nDays = uniqueN(dv_date),
                     sumkWh = sum(consumption_kWh),
                     halfHourly_meankWh = mean(consumption_kWh)),
                 keyby = .(month = lubridate::month(dv_date))]

makeFlexTable(t, cap = "Monthly stats")

Table 3.3: Monthly stats
month	nDays	sumkWh	halfHourly_meankWh
2.0	18	1,240.1	1.5
3.0	31	1,833.9	1.3
4.0	30	1,689.1	1.2
5.0	31	1,385.4	0.9
6.0	15	632.9	0.9

message("Total gas to date")

## Total gas to date

sum(t$sumkWh)

## [1] 6781.409

t <- gasCons_dt[, .(nDays = uniqueN(dv_date),
                     sumkWh = sum(consumption_kWh)),
                 keyby = .(dv_date)]
message("Projected gas annual total")

## Projected gas annual total

mean(t$sumkWh)*365

## [1] 19801.71

Figure 3.5 shows half-hourly gas import (‘consumption’) for the current year. The power cuts are even easier to see here. Interestingly the pattern after the gas boiler was serviced in June is more varied. What did he change?

ggplot2::ggplot(gasCons_dt, aes(x = dv_date, y = dv_hms, fill = consumption_kWh)) +
  geom_tile() +
  theme(legend.position = "bottom") +
  scale_fill_viridis_c(name = "Gas import (kWh)") +
  labs(x = "Date",
       y = "Half-hour")

Figure 3.5: Half-hourly gas consumption (current year)

Repeat but with just the last 7 days of data - useful for checking recent appliance use and offspring effects.

today <- lubridate::today()
p <- ggplot2::ggplot(gasCons_dt[dv_date >= today - 7], aes(x = dv_date, y = dv_hms, fill = consumption_kWh)) +
  geom_tile() +
  theme(legend.position = "bottom") +
  scale_fill_viridis_c(name = "Gas import (kWh)") +
  labs(x = "Date",
       y = "Half-hour")

plotly::ggplotly(p)

Figure 3.6: Half hourly gas import (current year, last 7 days)

Figure ?? shows the mean daily kWh import with a smoothed curve.

To do: mark weekends etc

plotDT <- gasCons_dt[, .(sum_kWh = sum(consumption_kWh),
                 mean_kWh = mean(consumption_kWh),
                 nObs = .N), keyby = .(dv_date, dv_weekend)]

ggplot2::ggplot(plotDT, aes(x = dv_date, y = sum_kWh, 
                            colour = dv_weekend)) +
  geom_point() +
  geom_smooth() +
  theme(legend.position = "bottom") +
  guides(colour = guide_legend (ncol = 3)) +
  scale_colour_viridis_d(name = "Weekend") +
  labs(x = "Date",
       y = "Sum kWh per day")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 3.7: Daily gas consumption (current year)

ggplot2::ggplot(plotDT, aes(x = dv_date, y = mean_kWh, 
                            colour = dv_weekend)) +
  geom_point() +
  geom_smooth() +
  theme(legend.position = "bottom") +
  guides(colour = guide_legend (ncol = 3)) +
  scale_colour_viridis_d(name = "Weekend") +
  labs(x = "Date",
       y = "Mean kWh per day")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 3.8: Daily gas consumption (current year)

Figure 3.9 shows the mean daily kWh import with a smoothed curve by period of the day.

To do: mark weekends etc

plotDT <- gasCons_dt[, .(sum_kWh = sum(consumption_kWh),
                 mean_kWh = mean(consumption_kWh),
                 nObs = .N), keyby = .(dv_date, dv_peakPeriod, dv_weekend)]

ggplot2::ggplot(plotDT, aes(x = dv_date, y = mean_kWh, 
                            colour = dv_peakPeriod)) +
  geom_line() +
  geom_smooth() +
  #facet_grid(dv_peakPeriod ~ .) +
  theme(legend.position = "bottom") +
  guides(colour = guide_legend (ncol = 3)) +
  scale_colour_viridis_d(name = "Peak period") +
  labs(x = "Date",
       y = "Mean kWh per period")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 3.9: Daily gas consumption by peak period (current year)

4 Annexes

4.1 Data descriptions

4.1.1 Electricity consumption

Use skmir::skim()to summarise.

skimr::skim(elecCons_dt)

Table 4.1: Data summary
Name	elecCons_dt
Number of rows	7968
Number of columns	9
Key	NULL
_______________________
Column type frequency:
character	3
Date	1
difftime	1
factor	1
numeric	2
POSIXct	1
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
interval_start	1	20	25	7968
interval_end	1	20	25	7968
dv_weekend	1	6	8	3

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
dv_date	0	1	2022-01-01	2022-06-15	2022-03-24	166

Variable type: difftime

skim_variable	n_missing	complete_rate	min	max	median	n_unique
dv_hms	0	1	0 secs	84600 secs	42300 secs	48

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
dv_peakPeriod	0	1	FALSE	5	Ear: 2324, Day: 2324, Eve: 1328, Lat: 1328

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
consumption	0	1	0.22	0.22	0	0.09	0.14	0.28	1.97	▇▁▁▁▁
consumption_kWh	0	1	0.22	0.22	0	0.09	0.14	0.28	1.97	▇▁▁▁▁

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
dv_start	0	1	2022-01-01	2022-06-15 23:30:00	2022-03-24 23:45:00	7968

4.1.2 Gas consumption